﻿* Encoding: UTF-8.
* Encoding: .

/* Runs against datafile JonesMitchellMartin_NUQ_Data.sav containing data obtained and owned by
/* Northwestern Universitiy, Qatar (NUQ)

/* "Ambivalent Sexism? Shifting Patterns of Gender Bias in Five Arab Countries"
/* Calvert W. Jones, Jocelyn Sage Mitchell, and Justin D. Martin

weight by weight.

/* compute variables, omitting "don't know" and "refused to answer" 

IF  (ExpectSheCares = 1 or ExpectHeCares = 1) JournoCares_omit=1.
IF  (ExpectSheCares = 2 or ExpectHeCares = 2) JournoCares_omit=2.
IF  (ExpectSheCares = 3 or ExpectHeCares = 3) JournoCares_omit=3.
IF  (ExpectSheCares = 4 or ExpectHeCares = 4) JournoCares_omit=4.
IF  (ExpectSheCares = 5 or ExpectHeCares = 5) JournoCares_omit=5.

IF  (ExpectSheisUnbiased = 1 or ExpectHeisUnbiased = 1) JournoUnbiased_omit=1.
IF  (ExpectSheisUnbiased = 2 or ExpectHeisUnbiased = 2) JournoUnbiased_omit=2.
IF  (ExpectSheisUnbiased = 3 or ExpectHeisUnbiased = 3) JournoUnbiased_omit=3.
IF  (ExpectSheisUnbiased = 4 or ExpectHeisUnbiased = 4) JournoUnbiased_omit=4.
IF  (ExpectSheisUnbiased = 5 or ExpectHeisUnbiased = 5) JournoUnbiased_omit=5.


IF  (ExpectSheDoesntKnow = 1 or ExpectHeDoesntKnow = 1) JournoDoesntKnowMuch_omit=1.
IF  (ExpectSheDoesntKnow = 2 or ExpectHeDoesntKnow  = 2) JournoDoesntKnowMuch_omit=2.
IF  (ExpectSheDoesntKnow = 3 or ExpectHeDoesntKnow  = 3) JournoDoesntKnowMuch_omit=3.
IF  (ExpectSheDoesntKnow = 4 or ExpectHeDoesntKnow  = 4) JournoDoesntKnowMuch_omit=4.
IF  (ExpectSheDoesntKnow = 5 or ExpectHeDoesntKnow  = 5) JournoDoesntKnowMuch_omit=5.

/* reverse score expertise dv

If (JournoDoesntKnowMuch_omit = 1) JournoDoesntKnowMuch_r=5.
If (JournoDoesntKnowMuch_omit = 2) JournoDoesntKnowMuch_r=4.
If (JournoDoesntKnowMuch_omit = 3) JournoDoesntKnowMuch_r=3.
If (JournoDoesntKnowMuch_omit = 4) JournoDoesntKnowMuch_r=2.
If (JournoDoesntKnowMuch_omit = 5) JournoDoesntKnowMuch_r=1.

value labels
JournoCares_omit
JournoUnbiased_omit
JournoDoesntKnowMuch_r
1 'Strongly disagree'
2 'Somewhat disagree'
3 'Neutral'
4 'Somewhat agree'
5 'Strongly agree'.
Execute.


/* Compute "news media reliance" variable

if (q39A = 1) q39A_interest_omit = 1.
if (q39A = 2) q39A_interest_omit = 2.
if (q39A = 3) q39A_interest_omit = 3.
if (q39A = 4) q39A_interest_omit = 4.
if (q39A = 5) q39A_interest_omit = 5.
if (q39A = 6) q39A_interest_omit = 6.
Execute.

IF  (Q39A_interest_omit < 3) q39A_interest_bal=1.
IF  (Q39A_interest_omit = 3 or Q39A_interest_omit = 4) q39A_interest_bal=2.
IF  (Q39A_interest_omit = 5 or Q39A_interest_omit = 6) q39A_interest_bal=3.

value labels
q39A_interest_omit
1 'Never'
2 'Less than once a month'
3 'Monthly'
4 'Weekly'
5 'Once a day'
6 'Several times a day'.
Execute.


 * /* compute income levels by country 

if (q51UAE < 8) q51_income_bal = 1.
if (q51UAE = 8) q51_income_bal = 2.
if (q51UAE = 9) q51_income_bal = 2.
if (q51UAE = 10) q51_income_bal = 3.
if (q51UAE = 11) q51_income_bal = 3.
if (q51UAE = 12 ) q51_income_bal = 3.

if (q51Lebanon < 5) q51_income_bal = 1.
if (q51Lebanon = 5) q51_income_bal = 2.
if (q51Lebanon = 6) q51_income_bal = 2.
if (q51Lebanon = 7) q51_income_bal = 2.
if (q51Lebanon = 8) q51_income_bal = 3.
if (q51Lebanon = 9) q51_income_bal = 3.
if (q51Lebanon = 10) q51_income_bal = 3.
if (q51Lebanon = 11) q51_income_bal = 3.
if (q51Lebanon = 12) q51_income_bal = 3.

if (q51Tunisia < 5) q51_income_bal = 1.
if (q51Tunisia = 5) q51_income_bal = 2.
if (q51Tunisia = 6) q51_income_bal = 2.
if (q51Tunisia = 7) q51_income_bal = 3. 
if (q51Tunisia = 8) q51_income_bal = 3.
if (q51Tunisia = 9) q51_income_bal = 3. 
if (q51Tunisia = 10) q51_income_bal = 3. 
if (q51Tunisia = 11) q51_income_bal = 3.
if (q51Tunisia = 12) q51_income_bal = 3.

if (q51KSA < 4) q51_income_bal = 1.
if (q51KSA = 4) q51_income_bal = 2.
if (q51KSA = 5) q51_income_bal = 2.
if (q51KSA = 6) q51_income_bal = 3. 
if (Q51KSA = 7) q51_income_bal = 3.
if (Q51KSA = 8) q51_income_bal = 3.
if (Q51KSA = 9) q51_income_bal = 3.
if (Q51KSA = 10) q51_income_bal = 3.
if (Q51KSA = 11) q51_income_bal = 3.
if (Q51KSA = 12) q51_income_bal = 3.

value labels
    q51_income_bal
    1 'Low'
    2 'Middle'
    3 'High'.
Execute.

value labels
q39A_interest_bal
1 'Low interest 1-2'
2 'Moderately interested 3-4'
3 'High interest 5-6'.
Execute.

/*create variable to indicate Gulf country or Non-Gulf country

if (q2 = 3) gulf = 1.
if (q2 = 4) gulf = 1.
if (q2 = 6) gulf = 1. 
if (q2 = 2) gulf = 0.
if (q2 = 5) gulf = 0.


value labels
gulf
0 'Non-Gulf States'
1 'Gulf States'.
Execute.

/* compute age groups (omitting respondents under 18)

if (q4> 17) age_omit = q4.
IF  (age_omit > 17 and age_omit<27) agegroup_bal=1.
IF  (age_omit > 26 and age_omit<40) agegroup_bal=2.
IF  (age_omit > 39) agegroup_bal=3.

value labels
agegroup_bal
1 '18-26'
2 '27-39'
3 '40 and over'.
Execute.


/*compute ideology grouping variable

IF  (Q48 = 1 or q48 = 2) q48_ideology=1.
IF  (Q48 = 3) q48_ideology=2.
IF  (Q48 = 4 or q48 = 5) q48_ideology=3.

VALUE LABELS
q48_ideology
1 'Conservative'
2 'Neither'
3 'Progressive'.
Execute.

/* create employment status variable

IF (Q6 = 1) Q6_employed = 1.
IF (Q6 = 2) Q6_employed = 0.
execute.

VALUE LABELS
Q6_employed
1 'Employed'
0 'Not employed'.
Execute.

/* change gender codes to 0 and 1 for interviewer gender.

If (q55=1) Q55_int_gen=0.
if (q55=2) Q55_int_gen=1.
Execute.

/* change gender codes to 0 and 1 for respondent gender.

if (q3_Genderofrespondent = 1) q3_resp_gen = 0.
if (q3_Genderofrespondent = 2) q3_resp_gen = 1.

value labels
Q55_int_gen
0 'Male interviewer'
1 'Female interviewer'.
Execute.

value labels
Q3_resp_gen
0 'Male Respondent'
1 'Female Respondent'.
Execute.

/* compute education level groups

IF (Q50 <= 5) Q50_educ_bal= 1.
IF (Q50 = 6)  Q50_educ_bal= 2.
if (q50 = 7) Q50_educ_bal= 2.
if (q50 = 8) Q50_educ_bal= 2.
IF (Q50 = 9)  Q50_educ_bal= 3.
IF (q50 = 10) Q50_educ_bal= 3.
Execute.

Value labels
q50_educ_bal
1 'Intermediate or less 1-5'
2 'Higher than intermediate but did not complete college 6-8'
3 'Bachelors degree or higher'.
Execute.

/* compute religiosity groups

IF (Q49 < 4) Q49_relig = 1.
if (q49 = 4 or q49 = 5 or q49 = 6) q49_relig = 2.
If (q49 = 7 or q49 = 8 or q49 = 9)  q49_relig = 3.
Execute.

VALUE LABELS
Q49_relig
1 'Low'
2 'Moderate'
3 'High'.
Execute.




/* create variable labels

variable labels
q2 'Country'
q3_resp_gen 'Respondent Gender'
Q55_int_gen 'Interviewer Gender'
q6_employed 'Employment status'
age_omit 'Age'
agegroup_bal 'Age group'
Q50_educ_bal 'Education'
q49_relig 'Religiosity'
q48_ideology 'Ideology'
q39A_interest_bal 'News media reliance'
q51_income_bal 'Income'
JournoCares_omit 'Cares'
JournoUnbiased_omit 'Trustworthiness'
JournoDoesntKnowMuch_r 'Expertise'.
Execute.

VARIABLE LEVEL
agegroup_bal q50_educ_bal q49_relig q48_ideology q39A_interest_bal q51_income_bal JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_omit JournoDoesntKnowMuch_r (SCALE)
/q3_resp_gen q55_int_gen q6_employed gulf (NOMINAL).
Execute.


/* t tests for dependent variables, aggregate for 5 countries

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).


/* t-test for male respondents only, five countries

USE ALL.
COMPUTE filter_$=(q3_resp_gen=0).
VARIABLE LABELS filter_$ 'q3_resp_gen = 0 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).

/* Figure 1 Benevolent sexism among men in five countries

GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=MEANSE(JournoCares_omit, 1) 
    MEANSE(JournoUnbiased_omit, 1) MEANSE(JournoDoesntKnowMuch_r, 1) GenderofJourno MISSING=LISTWISE 
    REPORTMISSING=NO 
    TRANSFORM=VARSTOCASES(SUMMARY="#SUMMARY" INDEX="#INDEX" LOW="#LOW" HIGH="#HIGH") 
  /GRAPHSPEC SOURCE=INLINE 
  /COLORCYCLE COLOR1(90,100,94), COLOR2(204,204,204), COLOR3(159,24,83), COLOR4(250,77,86), 
    COLOR5(87,4,8), COLOR6(25,128,56), COLOR7(0,45,156), COLOR8(238,83,139), COLOR9(178,134,0), 
    COLOR10(0,157,154), COLOR11(1,39,73), COLOR12(138,56,0), COLOR13(165,110,255), 
    COLOR14(236,230,208), COLOR15(69,70,71), COLOR16(92,202,136), COLOR17(208,83,52), 
    COLOR18(204,127,228), COLOR19(225,188,29), COLOR20(237,75,75), COLOR21(28,205,205), 
    COLOR22(92,113,72), COLOR23(225,139,14), COLOR24(9,38,114), COLOR25(90,100,94), COLOR26(155,0,0), 
    COLOR27(207,172,227), COLOR28(150,145,145), COLOR29(63,235,124), COLOR30(105,41,196) 
  /FRAME OUTER=NO INNER=NO 
  /GRIDLINES XAXIS=NO YAXIS=YES. 
BEGIN GPL 
  SOURCE: s=userSource(id("graphdataset")) 
  DATA: SUMMARY=col(source(s), name("#SUMMARY")) 
  DATA: INDEX=col(source(s), name("#INDEX"), unit.category()) 
  DATA: GenderofJourno=col(source(s), name("GenderofJourno"), unit.category()) 
  DATA: LOW=col(source(s), name("#LOW")) 
  DATA: HIGH=col(source(s), name("#HIGH")) 
  COORD: rect(dim(1,2), cluster(3,0)) 
  GUIDE: axis(dim(2), delta(.25), label("Mean Scores")) 
  GUIDE: legend(aesthetic(aesthetic.color.interior), label("Gender of Journalist")) 
  SCALE: cat(dim(3), include("0", "1", "2")) 
  SCALE: linear(dim(2), min(2.75), max(3.75)) 
  SCALE: cat(aesthetic(aesthetic.color.interior), include("0", "1")) 
  SCALE: cat(dim(1), include("0", "1")) 
  ELEMENT: interval(position(GenderofJourno*SUMMARY*INDEX), color.interior(GenderofJourno), 
    shape.interior(shape.square)) 
  ELEMENT: interval(position(region.spread.range(GenderofJourno*(LOW+HIGH)*INDEX)), 
    shape.interior(shape.ibeam)) 
END GPL.

/* Model 1, Men in five countries, cares, Appendix D
 
REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT JournoCares_omit
  /METHOD=ENTER GenderofJourno Q50_educ_bal age_omit  q48_ideology Q49_relig
    Q6_employed q39A_interest_bal     Q55_int_gen.

/* Qatar, Saudi Arabia, and UAE, men only

USE ALL.
COMPUTE filter_$=(gulf = 1  and q3_resp_gen = 0).
VARIABLE LABELS filter_$ 'gulf = 1  and q3_resp_gen = 0 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).

/* Model 2 Gulf men, expertise (Appendix D)

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT Journodoesntknowmuch_r
  /METHOD=ENTER GenderofJourno  Q50_educ_bal age_omit  q48_ideology Q49_relig
    Q6_employed q39A_interest_bal     Q55_int_gen.

/* Lebanon and Tunisia, men only


USE ALL.
COMPUTE filter_$=(gulf = 0 and  q3_resp_gen = 0).
VARIABLE LABELS filter_$ 'gulf = 0 and  q3_resp_gen = 0 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).

/* Men in Gulf States vs Non-Gulf States

USE ALL.
COMPUTE filter_$=(q3_resp_gen = 0).
VARIABLE LABELS filter_$ 'q3_resp_gen = 0(FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

UNIANOVA JournoDoesntKnowMuch_r  BY gulf GenderofJourno 
  /METHOD=SSTYPE(3) 
  /INTERCEPT=INCLUDE 
   /print=DESCRIPTIVE
  /CRITERIA=ALPHA(0.05) 
  /DESIGN=GenderofJourno gulf GenderofJourno*gulf.


* Figure 2 Hostile sexism among men in the Gulf vs Non-Gulf States
* Chart Builder. 

GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=gulf MEANSE(JournoDoesntKnowMuch_r, 
    1)[name="MEAN_JournoDoesntKnowMuch_r" LOW="MEAN_JournoDoesntKnowMuch_r_LOW" 
    HIGH="MEAN_JournoDoesntKnowMuch_r_HIGH"] GenderofJourno MISSING=LISTWISE REPORTMISSING=NO 
  /GRAPHSPEC SOURCE=INLINE 
  /COLORCYCLE COLOR1(102,102,102), COLOR2(204,204,204), COLOR3(159,24,83), COLOR4(250,77,86), 
    COLOR5(87,4,8), COLOR6(25,128,56), COLOR7(0,45,156), COLOR8(238,83,139), COLOR9(178,134,0), 
    COLOR10(0,157,154), COLOR11(1,39,73), COLOR12(138,56,0), COLOR13(165,110,255), 
    COLOR14(236,230,208), COLOR15(69,70,71), COLOR16(92,202,136), COLOR17(208,83,52), 
    COLOR18(204,127,228), COLOR19(225,188,29), COLOR20(237,75,75), COLOR21(28,205,205), 
    COLOR22(92,113,72), COLOR23(225,139,14), COLOR24(9,38,114), COLOR25(90,100,94), COLOR26(155,0,0), 
    COLOR27(207,172,227), COLOR28(150,145,145), COLOR29(63,235,124), COLOR30(105,41,196) 
  /FRAME OUTER=NO INNER=NO 
  /GRIDLINES XAXIS=NO YAXIS=YES. 
BEGIN GPL 
  SOURCE: s=userSource(id("graphdataset")) 
  DATA: gulf=col(source(s), name("gulf"), unit.category()) 
  DATA: MEAN_JournoDoesntKnowMuch_r=col(source(s), name("MEAN_JournoDoesntKnowMuch_r")) 
  DATA: GenderofJourno=col(source(s), name("GenderofJourno"), unit.category()) 
  DATA: LOW=col(source(s), name("MEAN_JournoDoesntKnowMuch_r_LOW")) 
  DATA: HIGH=col(source(s), name("MEAN_JournoDoesntKnowMuch_r_HIGH")) 
  COORD: rect(dim(1,2), cluster(3,0)) 
  GUIDE: axis(dim(2), delta(.1), label("Mean Expertise Scores")) 
  GUIDE: legend(aesthetic(aesthetic.color.interior), label("Gender of Journalist")) 
  SCALE: cat(dim(3), include("1.00", "0.00"), sort.values("1.00", "0.00")) 
  SCALE: linear(dim(2), min(2.5), max(3.3)) 
  SCALE: cat(aesthetic(aesthetic.color.interior), include("0", "1")) 
  SCALE: cat(dim(1), include("0", "1")) 
  ELEMENT: interval(position(GenderofJourno*MEAN_JournoDoesntKnowMuch_r*gulf), 
    color.interior(GenderofJourno), shape.interior(shape.square)) 
  ELEMENT: interval(position(region.spread.range(GenderofJourno*(LOW+HIGH)*gulf)), 
    shape.interior(shape.ibeam)) 
END GPL.

/* Saudi men

USE ALL.
COMPUTE filter_$=(q2 = 4 and q3_resp_gen = 0).
VARIABLE LABELS filter_$ 'q2 = 4 and q3_resp_gen = 0 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).

/* Figure 3. Ambivalent sexism among Saudi men


GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=MEANSE(JournoCares_omit, 1) 
    MEANSE(JournoUnbiased_omit, 1) MEANSE(JournoDoesntKnowMuch_r, 1) GenderofJourno MISSING=LISTWISE 
    REPORTMISSING=NO 
    TRANSFORM=VARSTOCASES(SUMMARY="#SUMMARY" INDEX="#INDEX" LOW="#LOW" HIGH="#HIGH") 
  /GRAPHSPEC SOURCE=INLINE 
  /COLORCYCLE COLOR1(90,100,94), COLOR2(204,204,204), COLOR3(159,24,83), COLOR4(250,77,86), 
    COLOR5(87,4,8), COLOR6(25,128,56), COLOR7(0,45,156), COLOR8(238,83,139), COLOR9(178,134,0), 
    COLOR10(0,157,154), COLOR11(1,39,73), COLOR12(138,56,0), COLOR13(165,110,255), 
    COLOR14(236,230,208), COLOR15(69,70,71), COLOR16(92,202,136), COLOR17(208,83,52), 
    COLOR18(204,127,228), COLOR19(225,188,29), COLOR20(237,75,75), COLOR21(28,205,205), 
    COLOR22(92,113,72), COLOR23(225,139,14), COLOR24(9,38,114), COLOR25(90,100,94), COLOR26(155,0,0), 
    COLOR27(207,172,227), COLOR28(150,145,145), COLOR29(63,235,124), COLOR30(105,41,196) 
  /FRAME OUTER=NO INNER=NO 
  /GRIDLINES XAXIS=NO YAXIS=YES. 
BEGIN GPL 
  SOURCE: s=userSource(id("graphdataset")) 
  DATA: SUMMARY=col(source(s), name("#SUMMARY")) 
  DATA: INDEX=col(source(s), name("#INDEX"), unit.category()) 
  DATA: GenderofJourno=col(source(s), name("GenderofJourno"), unit.category()) 
  DATA: LOW=col(source(s), name("#LOW")) 
  DATA: HIGH=col(source(s), name("#HIGH")) 
  COORD: rect(dim(1,2), cluster(3,0)) 
  GUIDE: axis(dim(2), delta(.1), label("Mean Scores")) 
  GUIDE: legend(aesthetic(aesthetic.color.interior), label("Gender of Journalist")) 
  SCALE: cat(dim(3), include("0", "1", "2")) 
  SCALE: linear(dim(2), min(2.5), max(3.90)) 
  SCALE: cat(aesthetic(aesthetic.color.interior), include("0", "1")) 
  SCALE: cat(dim(1), include("0", "1")) 
  ELEMENT: interval(position(GenderofJourno*SUMMARY*INDEX), color.interior(GenderofJourno), 
    shape.interior(shape.square)) 
  ELEMENT: interval(position(region.spread.range(GenderofJourno*(LOW+HIGH)*INDEX)), 
    shape.interior(shape.ibeam)) 
END GPL.


/* Model 3 Saudi men, cares (Appendix D)

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT JournoCares_omit
  /METHOD=ENTER GenderofJourno  Q50_educ_bal age_omit  q48_ideology Q49_relig
    Q6_employed q39A_interest_bal     Q55_int_gen.

/* Model 4 Saudi men, expertise (Appendix D)

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT JournoDoesntKnowMuch_r
  /METHOD=ENTER GenderofJourno  Q50_educ_bal age_omit  q48_ideology Q49_relig
    Q6_employed q39A_interest_bal     Q55_int_gen.


/*Saudi women

USE ALL.
COMPUTE filter_$=(q2 = 4 and q3_resp_gen = 1).
VARIABLE LABELS filter_$ 'q2 = 4 and q3_resp_gen = 1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).

/* Model 5, Saudi women, cares (Appendix D)

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT JournoCares_omit
  /METHOD=ENTER GenderofJourno  Q50_educ_bal age_omit  q48_ideology Q49_relig
    Q6_employed q39A_interest_bal     Q55_int_gen.


/* Saudi men vs Saudi women, cares

USE ALL.
COMPUTE filter_$=(q2 = 4).
VARIABLE LABELS filter_$ 'q2 = 4 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

UNIANOVA JournoCares_omit BY GenderofJourno q3_resp_gen
  /METHOD=SSTYPE(3) 
  /INTERCEPT=INCLUDE 
  /print=DESCRIPTIVE
  /CRITERIA=ALPHA(0.05) 
  /DESIGN=GenderofJourno q3_resp_gen GenderofJourno*q3_resp_gen.

/* Figure 4 Saudi men vs Saudi women
/* Chart Builder. 
* Chart Builder. 
GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=q3_resp_gen MEANSE(JournoCares_omit, 
    1)[name="MEAN_JournoCares_omit" LOW="MEAN_JournoCares_omit_LOW" HIGH="MEAN_JournoCares_omit_HIGH"] 
    GenderofJourno MISSING=LISTWISE REPORTMISSING=NO 
  /GRAPHSPEC SOURCE=INLINE 
  /COLORCYCLE COLOR1(102,102,102), COLOR2(204,204,204), COLOR3(159,24,83), COLOR4(250,77,86), 
    COLOR5(87,4,8), COLOR6(25,128,56), COLOR7(0,45,156), COLOR8(238,83,139), COLOR9(178,134,0), 
    COLOR10(0,157,154), COLOR11(1,39,73), COLOR12(138,56,0), COLOR13(165,110,255), 
    COLOR14(236,230,208), COLOR15(69,70,71), COLOR16(92,202,136), COLOR17(208,83,52), 
    COLOR18(204,127,228), COLOR19(225,188,29), COLOR20(237,75,75), COLOR21(28,205,205), 
    COLOR22(92,113,72), COLOR23(225,139,14), COLOR24(9,38,114), COLOR25(90,100,94), COLOR26(155,0,0), 
    COLOR27(207,172,227), COLOR28(150,145,145), COLOR29(63,235,124), COLOR30(105,41,196) 
  /FRAME OUTER=NO INNER=NO 
  /GRIDLINES XAXIS=NO YAXIS=YES. 
BEGIN GPL 
  SOURCE: s=userSource(id("graphdataset")) 
  DATA: q3_resp_gen=col(source(s), name("q3_resp_gen"), unit.category()) 
  DATA: MEAN_JournoCares_omit=col(source(s), name("MEAN_JournoCares_omit")) 
  DATA: GenderofJourno=col(source(s), name("GenderofJourno"), unit.category()) 
  DATA: LOW=col(source(s), name("MEAN_JournoCares_omit_LOW")) 
  DATA: HIGH=col(source(s), name("MEAN_JournoCares_omit_HIGH")) 
  COORD: rect(dim(1,2), cluster(3,0)) 
  GUIDE: axis(dim(3), label("Respondent Gender")) 
  GUIDE: axis(dim(2), delta(.1), label("Mean Cares Scores")) 
  GUIDE: legend(aesthetic(aesthetic.color.interior), label("Gender of Journalist")) 
  SCALE: cat(dim(3), include("0.00", "1.00"), sort.values("0.00", "1.00")) 
  SCALE: linear(dim(2), min(3.0), max(3.8)) 
  SCALE: cat(aesthetic(aesthetic.color.interior), include("0", "1")) 
  SCALE: cat(dim(1), include("0", "1")) 
  ELEMENT: interval(position(GenderofJourno*MEAN_JournoCares_omit*q3_resp_gen), 
    color.interior(GenderofJourno), shape.interior(shape.square)) 
  ELEMENT: interval(position(region.spread.range(GenderofJourno*(LOW+HIGH)*q3_resp_gen)), 
    shape.interior(shape.ibeam)) 
END GPL.


/* Qatari men

USE ALL.
COMPUTE filter_$=(q2 = 3 and q3_resp_gen = 0).
VARIABLE LABELS filter_$ 'q2 = 3 and q3_resp_gen = 0 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).


/* Qatari women 

USE ALL.
COMPUTE filter_$=(q2 = 3 and q3_resp_gen = 1).
VARIABLE LABELS filter_$ 'q2 = 3 and q3_resp_gen = 1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).

/* Model 6 Qatari women, expertise (Appendix D)

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT JournoDoesntKnowMuch_r
  /METHOD=ENTER GenderofJourno Q50_educ_bal age_omit  q48_ideology Q49_relig
    Q6_employed q39A_interest_bal     Q55_int_gen.


/* Qatari men vs women, expertise

USE ALL.
COMPUTE filter_$=(q2 = 3).
VARIABLE LABELS filter_$ 'q2 = 3 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

UNIANOVA JournoDoesntKnowMuch_r BY GenderofJourno q3_resp_gen
  /METHOD=SSTYPE(3) 
  /INTERCEPT=INCLUDE 
  /print=DESCRIPTIVE
  /CRITERIA=ALPHA(0.05) 
  /DESIGN=GenderofJourno q3_resp_gen GenderofJourno*q3_resp_gen.

/* Figure 5 Qatari men vs Qatari women, expertise
 
 * Chart Builder. 

GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=q3_resp_gen MEANSE(JournoDoesntKnowMuch_r, 
    1)[name="MEAN_JournoDoesntKnowMuch_r" LOW="MEAN_JournoDoesntKnowMuch_r_LOW" 
    HIGH="MEAN_JournoDoesntKnowMuch_r_HIGH"] GenderofJourno MISSING=LISTWISE REPORTMISSING=NO 
  /GRAPHSPEC SOURCE=INLINE 
  /COLORCYCLE COLOR1(90,100,94), COLOR2(204,204,204), COLOR3(159,24,83), COLOR4(250,77,86), 
    COLOR5(87,4,8), COLOR6(25,128,56), COLOR7(0,45,156), COLOR8(238,83,139), COLOR9(178,134,0), 
    COLOR10(0,157,154), COLOR11(1,39,73), COLOR12(138,56,0), COLOR13(165,110,255), 
    COLOR14(236,230,208), COLOR15(69,70,71), COLOR16(92,202,136), COLOR17(208,83,52), 
    COLOR18(204,127,228), COLOR19(225,188,29), COLOR20(237,75,75), COLOR21(28,205,205), 
    COLOR22(92,113,72), COLOR23(225,139,14), COLOR24(9,38,114), COLOR25(90,100,94), COLOR26(155,0,0), 
    COLOR27(207,172,227), COLOR28(150,145,145), COLOR29(63,235,124), COLOR30(105,41,196) 
  /FRAME OUTER=NO INNER=NO 
  /GRIDLINES XAXIS=NO YAXIS=YES. 
BEGIN GPL 
  SOURCE: s=userSource(id("graphdataset")) 
  DATA: q3_resp_gen=col(source(s), name("q3_resp_gen"), unit.category()) 
  DATA: MEAN_JournoDoesntKnowMuch_r=col(source(s), name("MEAN_JournoDoesntKnowMuch_r")) 
  DATA: GenderofJourno=col(source(s), name("GenderofJourno"), unit.category()) 
  DATA: LOW=col(source(s), name("MEAN_JournoDoesntKnowMuch_r_LOW")) 
  DATA: HIGH=col(source(s), name("MEAN_JournoDoesntKnowMuch_r_HIGH")) 
  COORD: rect(dim(1,2), cluster(3,0)) 
  GUIDE: axis(dim(2), delta(.1), label("Mean Expertise Scores")) 
  GUIDE: legend(aesthetic(aesthetic.color.interior), label("Gender of Journalist")) 
  SCALE: cat(dim(3), sort.values("0", "1")) 
  SCALE: linear(dim(2), min(2.5), max(3.9)) 
  SCALE: cat(aesthetic(aesthetic.color.interior), include("0", "1")) 
  SCALE: cat(dim(1), include("0", "1")) 
  ELEMENT: interval(position(GenderofJourno*MEAN_JournoDoesntKnowMuch_r*q3_resp_gen), 
    color.interior(GenderofJourno), shape.interior(shape.square)) 
  ELEMENT: interval(position(region.spread.range(GenderofJourno*(LOW+HIGH)*q3_resp_gen)), 
    shape.interior(shape.ibeam)) 
END GPL.

/* Lebanese men and women

USE ALL.
COMPUTE filter_$=(q2 = 2).
VARIABLE LABELS filter_$ 'q2 = 2 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.


T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).

/* Model 7, Lebanese men and women, expertise (Appendix D)

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT JournoDoesntKnowMuch_r
  /METHOD=ENTER GenderofJourno  Q50_educ_bal age_omit  q48_ideology Q49_relig
    Q6_employed q39A_interest_bal     Q55_int_gen.


/* Lebanese men

USE ALL.
COMPUTE filter_$=(q2 = 2 and q3_resp_gen = 0).
VARIABLE LABELS filter_$ 'q2 = 2 and q3_resp_gen = 0 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).

/* Model 8 Lebanese men, expertise (Appendix D)

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT JournoDoesntKnowMuch_r
  /METHOD=ENTER GenderofJourno  Q50_educ_bal age_omit  q48_ideology Q49_relig
    Q6_employed q39A_interest_bal  Q55_int_gen.

/* Lebanese women

USE ALL.
COMPUTE filter_$=(q2 = 2 and q3_resp_gen = 1).
VARIABLE LABELS filter_$ 'q2 = 2 and q3_resp_gen = 1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=JournoCares_omit JournoUnbiased_omit JournoDoesntKnowMuch_r
  /CRITERIA=CI(.95).



/*Figure 6 Pro-female bias in Lebanon. (Note the graph in the paper also includes a category on the x axis for "All Lebanese" to include both men and women, for comparison.)
/* Note: The final Figure 6 graph combines the following two graphs into one graph for improved visualization.

USE ALL.
COMPUTE filter_$=(q2 = 2).
VARIABLE LABELS filter_$ 'q2 = 2 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

/* Figure 6 Male vs Female Respondents

/* Chart Builder.
GGRAPH
  /GRAPHDATASET NAME="graphdataset" VARIABLES=q3_resp_gen MEANSE(JournoDoesntKnowMuch_r,
    1)[name="MEAN_JournoDoesntKnowMuch_r" LOW="MEAN_JournoDoesntKnowMuch_r_LOW"
    HIGH="MEAN_JournoDoesntKnowMuch_r_HIGH"] GenderofJourno MISSING=LISTWISE REPORTMISSING=NO
  /GRAPHSPEC SOURCE=INLINE
  /COLORCYCLE COLOR1(102,102,102), COLOR2(204,204,204), COLOR3(159,24,83), COLOR4(250,77,86),
    COLOR5(87,4,8), COLOR6(25,128,56), COLOR7(0,45,156), COLOR8(238,83,139), COLOR9(178,134,0),
    COLOR10(0,157,154), COLOR11(1,39,73), COLOR12(138,56,0), COLOR13(165,110,255),
    COLOR14(236,230,208), COLOR15(69,70,71), COLOR16(92,202,136), COLOR17(208,83,52),
    COLOR18(204,127,228), COLOR19(225,188,29), COLOR20(237,75,75), COLOR21(28,205,205),
    COLOR22(92,113,72), COLOR23(225,139,14), COLOR24(9,38,114), COLOR25(90,100,94), COLOR26(155,0,0),
    COLOR27(207,172,227), COLOR28(150,145,145), COLOR29(63,235,124), COLOR30(105,41,196)
  /FRAME OUTER=NO INNER=NO
  /GRIDLINES XAXIS=NO YAXIS=YES.
BEGIN GPL
  SOURCE: s=userSource(id("graphdataset"))
  DATA: q3_resp_gen=col(source(s), name("q3_resp_gen"), unit.category())
  DATA: MEAN_JournoDoesntKnowMuch_r=col(source(s), name("MEAN_JournoDoesntKnowMuch_r"))
  DATA: GenderofJourno=col(source(s), name("GenderofJourno"), unit.category())
  DATA: LOW=col(source(s), name("MEAN_JournoDoesntKnowMuch_r_LOW"))
  DATA: HIGH=col(source(s), name("MEAN_JournoDoesntKnowMuch_r_HIGH"))
  COORD: rect(dim(1,2), cluster(3,0))
  GUIDE: axis(dim(2), label("Mean Expertise Score"))
  GUIDE: legend(aesthetic(aesthetic.color.interior), label("Gender of Journalist"))
  SCALE: cat(dim(3), include("0.00", "1.00"))
  SCALE: linear(dim(2), min(3.2), max(3.6))
  SCALE: cat(aesthetic(aesthetic.color.interior), include("0", "1"))
  SCALE: cat(dim(1), include("0", "1"))
  ELEMENT: interval(position(GenderofJourno*MEAN_JournoDoesntKnowMuch_r*q3_resp_gen),
    color.interior(GenderofJourno), shape.interior(shape.square))
  ELEMENT: interval(position(region.spread.range(GenderofJourno*(LOW+HIGH)*q3_resp_gen)),
    shape.interior(shape.ibeam))
END GPL.

/* Figure 6 All Lebanese segment of graph
    
    
/* Chart Builder.
GGRAPH
  /GRAPHDATASET NAME="graphdataset" VARIABLES=Q2 MEANSE(JournoDoesntKnowMuch_r,
    1)[name="MEAN_JournoDoesntKnowMuch_r" LOW="MEAN_JournoDoesntKnowMuch_r_LOW"
    HIGH="MEAN_JournoDoesntKnowMuch_r_HIGH"] GenderofJourno MISSING=LISTWISE REPORTMISSING=NO
  /GRAPHSPEC SOURCE=INLINE
  /COLORCYCLE COLOR1(102,102,102), COLOR2(204,204,204), COLOR3(159,24,83), COLOR4(250,77,86),
    COLOR5(87,4,8), COLOR6(25,128,56), COLOR7(0,45,156), COLOR8(238,83,139), COLOR9(178,134,0),
    COLOR10(0,157,154), COLOR11(1,39,73), COLOR12(138,56,0), COLOR13(165,110,255),
    COLOR14(236,230,208), COLOR15(69,70,71), COLOR16(92,202,136), COLOR17(208,83,52),
    COLOR18(204,127,228), COLOR19(225,188,29), COLOR20(237,75,75), COLOR21(28,205,205),
    COLOR22(92,113,72), COLOR23(225,139,14), COLOR24(9,38,114), COLOR25(90,100,94), COLOR26(155,0,0),
    COLOR27(207,172,227), COLOR28(150,145,145), COLOR29(63,235,124), COLOR30(105,41,196)
  /FRAME OUTER=NO INNER=NO
  /GRIDLINES XAXIS=NO YAXIS=YES.
BEGIN GPL
  SOURCE: s=userSource(id("graphdataset"))
  DATA: Q2=col(source(s), name("Q2"),
notIn("6", "5", "4", "3"), unit.category())
  DATA: MEAN_JournoDoesntKnowMuch_r=col(source(s), name("MEAN_JournoDoesntKnowMuch_r"))
  DATA: GenderofJourno=col(source(s), name("GenderofJourno"), unit.category())
  DATA: LOW=col(source(s), name("MEAN_JournoDoesntKnowMuch_r_LOW"))
  DATA: HIGH=col(source(s), name("MEAN_JournoDoesntKnowMuch_r_HIGH"))
  COORD: rect(dim(1,2), cluster(3,0))
  GUIDE: axis(dim(2), label("Mean Expertise Score"))
  GUIDE: legend(aesthetic(aesthetic.color.interior), label("Gender of Journalist"))
  GUIDE: text.subfootnote(label("Error Bars: +/- 1 SE"))
  SCALE: cat(dim(3), include("2"))
  SCALE: linear(dim(2), min(3.2), max(3.6))
  SCALE: cat(aesthetic(aesthetic.color.interior), include("0", "1"))
  SCALE: cat(dim(1), include("0", "1"))
  ELEMENT: interval(position(GenderofJourno*MEAN_JournoDoesntKnowMuch_r*Q2),
    color.interior(GenderofJourno), shape.interior(shape.square))
  ELEMENT: interval(position(region.spread.range(GenderofJourno*(LOW+HIGH)*Q2)),
    shape.interior(shape.ibeam))
END GPL.



/* Demographic Analysis and Modernization Theory: Interaction Effects

/*Figure 7  Modernization and pro-female bias  (Lebanon, Saudi Arabia, Tunisia, and the UAE),  income and journalist gender

USE ALL.
COMPUTE filter_$=(q2 ~=3).
VARIABLE LABELS filter_$ 'q2 ~=3 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.

UNIANOVA JournoCares_omit  BY GenderofJourno q51_income_bal
  /METHOD=SSTYPE(3)
  /INTERCEPT=INCLUDE
  /PLOT=PROFILE(q51_income_bal*GenderofJourno)
  /print=descriptive
  /CRITERIA=ALPHA(0.05)
  /DESIGN=GenderofJourno q51_income_bal GenderofJourno*q51_income_bal.
EXECUTE.



/* Figure 8. Rising education and pro-female bias in Qatar

USE ALL. 
COMPUTE filter_$=(q2 =3). 
VARIABLE LABELS filter_$ 'q2 =3 (FILTER)'. 
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. 
FORMATS filter_$ (f1.0). 
FILTER BY filter_$. 
EXECUTE. 


UNIANOVA JournoDoesntKnowMuch_r  BY GenderofJourno Q50_educ_bal
  /METHOD=SSTYPE(3)
  /INTERCEPT=INCLUDE
  /PLOT=PROFILE(Q50_educ_bal*GenderofJourno)
  /print=descriptive
  /CRITERIA=ALPHA(0.05)
  /DESIGN=GenderofJourno Q50_educ_bal GenderofJourno*Q50_educ_bal.
Execute.


/* Figure 9. Rising income and pro-female bias in Tunisia
    
USE ALL. 
COMPUTE filter_$=(q2=5). 
VARIABLE LABELS filter_$ 'q2=5 (FILTER)'. 
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. 
FORMATS filter_$ (f1.0). 
FILTER BY filter_$. 
EXECUTE. 
 
UNIANOVA JournoCares_omit  BY GenderofJourno Q51_income_bal 
  /METHOD=SSTYPE(3) 
  /INTERCEPT=INCLUDE 
  /PLOT=PROFILE(q51_income_bal*GenderofJourno) 
  /print=descriptive 
  /CRITERIA=ALPHA(0.05) 
  /DESIGN=GenderofJourno q51_income_bal GenderofJourno*q51_income_bal.
Execute.

/*Figure 10.  Youth and pro-female bias in Lebanon
    
  
USE ALL. 
COMPUTE filter_$=(q2=2). 
VARIABLE LABELS filter_$ 'q2=2 (FILTER)'. 
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. 
FORMATS filter_$ (f1.0). 
FILTER BY filter_$. 
EXECUTE. 
 
UNIANOVA JournoUnbiased_omit  BY GenderofJourno agegroup_bal 
  /METHOD=SSTYPE(3) 
  /INTERCEPT=INCLUDE 
  /PLOT=PROFILE(agegroup_bal*GenderofJourno) 
  /print=descriptive 
  /CRITERIA=ALPHA(0.05) 
  /DESIGN=GenderofJourno agegroup_bal GenderofJourno*agegroup_bal.
Execute.
    

/* Figure 11. Declining religiosity and pro-female bias in Qatar
    
USE ALL. 
COMPUTE filter_$=(q2=3). 
VARIABLE LABELS filter_$ 'q2=3 (FILTER)'. 
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. 
FORMATS filter_$ (f1.0). 
FILTER BY filter_$. 
EXECUTE. 
 
UNIANOVA JournoCares_omit  BY GenderofJourno Q49_relig 
  /METHOD=SSTYPE(3) 
  /INTERCEPT=INCLUDE 
  /PLOT=PROFILE(Q49_relig*GenderofJourno) 
  /print=descriptive 
  /CRITERIA=ALPHA(0.05) 
  /DESIGN=GenderofJourno Q49_relig GenderofJourno*Q49_relig.
Execute.
   
USE ALL.
EXECUTE.



/* Appendix A.1. Descriptive statistics and balance tests for five countries

USE ALL.
EXECUTE.

CROSSTABS
  /TABLES=q3_resp_gen Q55_int_gen Q6_employed BY GenderofJourno
  /FORMAT=AVALUE TABLES
  /STATISTICS=CHISQ
  /CELLS=COUNT COLUMN
  /COUNT ROUND CELL.

T-TEST GROUPS=GenderofJourno(0 1)
  /MISSING=ANALYSIS
  /VARIABLES=Q50_educ_bal Q49_relig age_omit q48_ideology q39A_interest_bal  
  /ES DISPLAY (FALSE)
  /CRITERIA=CI(.95).

/* Appendix D (see regression output associated with each figure above)
/* Appendix F (see statistical details associated with each figure above)..

